text: _base_ = [
      '_base_/datasets/coco_detection.py',
      '_base_/schedules/schedule_1x.py',
      '_base_/default_runtime.py'
  ]
  num_stages = 3
  num_proposals = 50
  model = dict(
      type='SparseRCNN',
      pretrained=None,
      backbone=dict(
          type='pvt_v2_b2',
          style='pytorch'),
      neck=dict(
          type='FPN',
          in_channels=[64, 128, 320, 512],
          out_channels=256,
          start_level=0,
          add_extra_convs='on_input',
          num_outs=4),
      rpn_head=dict(
          type='EmbeddingRPNHead',
          num_proposals=num_proposals,
          proposal_feature_channel=256),
      roi_head=dict(
          type='SparseRoIHead',
          num_stages=num_stages,
          stage_loss_weights=[1] * num_stages,
          proposal_feature_channel=256,
          bbox_roi_extractor=dict(
              type='SingleRoIExtractor',
              roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
              out_channels=256,
              featmap_strides=[4, 8, 16, 32]),
          bbox_head=[
              dict(
                  type='DIIHead',
                  num_classes=1,
                  num_ffn_fcs=2,
                  num_heads=8,
                  num_cls_fcs=1,
                  num_reg_fcs=3,
                  feedforward_channels=2048,
                  in_channels=256,
                  dropout=0.0,
                  ffn_act_cfg=dict(type='ReLU', inplace=True),
                  dynamic_conv_cfg=dict(
                      type='DynamicConv',
                      in_channels=256,
                      feat_channels=64,
                      out_channels=256,
                      input_feat_shape=7,
                      act_cfg=dict(type='ReLU', inplace=True),
                      norm_cfg=dict(type='LN')),
                  loss_bbox=dict(type='L1Loss', loss_weight=5.0),
                  loss_iou=dict(type='GIoULoss', loss_weight=2.0),
                  loss_cls=dict(
                      type='FocalLoss',
                      use_sigmoid=True,
                      gamma=2.0,
                      alpha=0.25,
                      loss_weight=2.0),
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      clip_border=False,
                      target_means=[0., 0., 0., 0.],
                      target_stds=[0.5, 0.5, 1., 1.])) for _ in range(num_stages)
          ]),
      # training and testing settings
      train_cfg=dict(
          rpn=None,
          rcnn=[
              dict(
                  assigner=dict(
                      type='HungarianAssigner',
                      cls_cost=dict(type='FocalLossCost', weight=2.0),
                      reg_cost=dict(type='BBoxL1Cost', weight=5.0),
                      iou_cost=dict(type='IoUCost', iou_mode='giou',
                                    weight=2.0)),
                  sampler=dict(type='PseudoSampler'),
                  pos_weight=1) for _ in range(num_stages)
          ]),
      test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_proposals)))
  
  # optimizer
  optimizer = dict(_delete_=True, type='AdamW', lr=0.00001 , weight_decay=0.0001)
  optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=1, norm_type=2))
  # learning policy
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='LoadAnnotations',
          with_bbox=True,
          with_mask=False,
          poly2mask=False),
      dict(
          type='Resize',
          img_scale=[(512, 640)],
          multiscale_mode='range',
          keep_ratio=True),
      dict(type='RandomFlip', flip_ratio=0.0),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(512, 640),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ])
  ]
  data = dict(
      samples_per_gpu=4,
      workers_per_gpu=8,
      train=dict(
          type='CocoDataset',
          classes=('table',),
          ann_file='/netscratch/minouei/alltables/filtered.json',
          img_prefix='/netscratch/minouei/alltables/images/',
          pipeline=train_pipeline),
      val=dict(
          type='CocoDataset',
          classes=('table',),
          ann_file='/netscratch/minouei/alltables/filteredval.json',
          img_prefix='/netscratch/minouei/alltables/images/',
          pipeline=test_pipeline),
      test=dict(
          type='CocoDataset',
          classes=('table',),
          ann_file='/netscratch/minouei/alltables/filteredval.json',
          img_prefix='/netscratch/minouei/alltables/images/',
          pipeline=test_pipeline))
  lr_config = dict(policy='step',warmup='linear',warmup_iters=1000,warmup_ratio=1.0 / 1000, step=[3, 5])
  runner = dict(type='EpochBasedRunner', max_epochs=1)
  log_config = dict(
      interval=150,
      hooks=[
          dict(type='TextLoggerHook'),
          dict(type='TensorboardLoggerHook')
      ])
  
  load_from = './work_dirs/sparse_rcnn_pvt_v2_b2_fpn_justpub1m/epoch_3.pth'
  